CN113011770A - Analysis method and analysis device for wind disaster vulnerability of power transmission tower - Google Patents
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Abstract
The application discloses an analysis method and an analysis device for wind damage vulnerability of a power transmission tower, wherein the analysis method comprises the steps of obtaining a wind speed time-course sample and a power transmission tower model, wherein the power transmission tower model comprises structural information and material information of the power transmission tower; processing the wind speed time course sample into a wind pressure time course, and carrying out nonlinear time course analysis on the power transmission tower model according to the wind pressure time course to obtain a probability wind disaster model, wherein the probability wind disaster model comprises a logarithmic mean value and a logarithmic standard deviation of a structural response peak value corresponding to the maximum wind speed; and calculating the failure probability of the power transmission tower under the maximum wind speed according to the probability wind disaster model. Through the method, the analysis process calculation is simple compared with the prior art, the analysis efficiency can be improved, and therefore the analysis of the wind damage vulnerability of the power transmission tower can be efficiently and conveniently obtained. In addition, the vulnerability curve obtained by the method is not jagged, so that additional smoothing treatment is not needed, and the steps are simpler.
Description
Technical Field
The application relates to the technical field of power transmission towers, in particular to an analysis method and an analysis device for wind damage vulnerability of a power transmission tower
Background
Vulnerability analysis concept comes from structural earthquake risk assessment, and since about 1980, scholars at home and abroad develop various earthquake vulnerability analysis methods. According to different structural damage data acquisition modes, the method mainly comprises an empirical vulnerability analysis method and a theoretical vulnerability analysis method.
The empirical vulnerability analysis is based on historical earthquake damage survey data, and an earthquake vulnerability curve is constructed by a mathematical statistic method, so that the method is suitable for areas which have historically suffered an earthquake and have more complete earthquake damage data. The theoretical vulnerability analysis method has stronger applicability, and the common methods are a cloud graph method and an incremental dynamic analysis method. The cloud graph method is also called as a probabilistic seismic demand analysis method, assumes that the structural seismic demand obeys log-normal distribution, constructs a statistical relationship between the structural seismic demand and seismic intensity through a linear regression technology, and calculates the seismic vulnerability under a given limit state by combining a probability distribution function of the structural seismic demand. The incremental dynamic analysis method obtains time-course samples of earthquakes with different intensities in a scaling mode, then carries out nonlinear time-course analysis to obtain the requirements of the structure earthquake, directly counts the exceeding probability of the structure under different limit states, and is widely applied to the earthquake hazard analysis of bridges in recent years.
At present, few researches on analysis of wind damage vulnerability of a power transmission tower are carried out, and in order to achieve certain calculation accuracy, more nonlinear time-course analysis needs to be carried out, which is time-consuming. In addition, the vulnerability curve obtained in the prior art is usually jagged, and the vulnerability curve is usually required to be smoothed, so that the processing steps are complicated.
Disclosure of Invention
The application provides an analysis method and an analysis device for wind damage of a power transmission tower, which aim to solve the problems of complex calculation and time consumption of analysis of the wind damage of the power transmission tower in the prior art.
In order to solve the technical problem, the application provides an analysis method for wind damage vulnerability of a power transmission tower, which comprises the following steps: acquiring a wind speed time-course sample and a power transmission tower model, wherein the power transmission tower model comprises the structural information and the material information of a power transmission tower; processing the wind speed time course sample into a wind pressure time course, and carrying out nonlinear time course analysis on the power transmission tower model according to the wind pressure time course to obtain a probability wind disaster model, wherein the probability wind disaster model comprises a logarithmic mean value and a logarithmic standard deviation of a structural response peak value corresponding to the maximum wind speed; and calculating the failure probability of the power transmission tower under the maximum wind speed according to the probability wind disaster model.
Optionally, obtaining wind speed time course samples comprises: selecting a plurality of wind speed time-course records which accord with a preset wind speed range from the historical typhoon monitoring data, wherein the maximum wind speed recorded by the wind speed time-course is within the preset wind speed range; processing the wind speed time course record into a wind speed time course sample v with the same time step length and durationk(t) (k is 1,2, …, N), N is the number of samples, and the peak value v of the wind speed time course of each wind speed time course sample is recordedp,k。
Optionally, performing nonlinear time course analysis on the power transmission tower model according to the wind pressure time course to obtain a probability wind disaster model, including: carrying out nonlinear time-course analysis on the power transmission tower model under each wind pressure time course by adopting a Newmark-beta method to obtain a structural response sample r of the power transmission towerk(t) (k ═ 1,2, …, N) and the corresponding structural response peak r is recordedp,k(ii) a Discrete points (lnv) are obtained in a logarithmic plane by taking the logarithm of the peak of the wind speed time course as an abscissa and the logarithm of the peak of the structural response as an ordinatep,k，lnrp,k) (k is 1,2, …, N) and obtaining structural response by linear regression methodThe log mean and log standard deviation of the peaks.
Optionally, the log mean of the structural response peaks is:
wherein a and b are linear regression coefficients, vpThe peak value of the wind speed time interval is obtained; the logarithmic standard deviation of the structural response peaks is:
optionally, the failure probability of the transmission tower at the maximum wind speed is:
wherein C is the index of the extreme state of the power transmission tower, mulnCAnd σlnCRespectively, the logarithmic mean and the logarithmic standard deviation of the extreme state index.
In order to solve the above technical problem, the present application provides an analysis device for wind damage vulnerability of a power transmission tower, including: the preprocessing unit is used for obtaining a wind speed time-course sample and a power transmission tower model, wherein the power transmission tower model comprises structural information and material information of a power transmission tower; the probability wind disaster model unit is used for processing the wind speed time interval sample into a wind pressure time interval and carrying out nonlinear time interval analysis on the power transmission tower model according to the wind pressure time interval to obtain a probability wind disaster model, wherein the probability wind disaster model comprises a logarithmic mean value and a logarithmic standard deviation of a structural response peak value corresponding to the maximum wind speed; and the analysis unit is used for calculating the failure probability of the power transmission tower under the maximum wind speed according to the probability wind disaster model.
Optionally, the preprocessing unit is further configured to select a plurality of wind speed time-course records meeting a preset wind speed range from the monitoring data of the historical typhoon, where a maximum wind speed recorded by the wind speed time-course is within the preset wind speed range; processing the wind speed time course record into a wind speed time course sample v with the same time step length and durationk(t) (k is 1,2, …, N), N is the number of samples, and the peak value v of the wind speed time course of each wind speed time course sample is recordedp,k。
Optionally, the probabilistic wind damage model unit is also usedCarrying out nonlinear time-course analysis on the power transmission tower model under each wind pressure time course by adopting a Newmark-beta method to obtain a structural response sample r of the power transmission towerk(t) (k ═ 1,2, …, N) and the corresponding structural response peak r is recordedp,k(ii) a Discrete points (lnv) are obtained in a logarithmic plane by taking the logarithm of the peak of the wind speed time course as an abscissa and the logarithm of the peak of the structural response as an ordinatep,k，lnrp,k) (k-1, 2, …, N) and obtaining the log mean and log standard deviation of the structural response peak by using a linear regression method.
Optionally, the log mean of the structural response peaks is:
wherein a and b are linear regression coefficients, vpThe peak value of the wind speed time interval is obtained; the logarithmic standard deviation of the structural response peaks is:
optionally, the failure probability of the transmission tower at the maximum wind speed is:
wherein C is the index of the extreme state of the power transmission tower, mulnCAnd σlnCRespectively, the logarithmic mean and the logarithmic standard deviation of the extreme state index.
The application provides an analysis method and an analysis device for wind damage vulnerability of a power transmission tower, wherein the analysis method comprises the steps of obtaining a wind speed time-course sample and a power transmission tower model, wherein the power transmission tower model comprises structural information and material information of the power transmission tower; processing the wind speed time course sample into a wind pressure time course, and carrying out nonlinear time course analysis on the power transmission tower model according to the wind pressure time course to obtain a probability wind disaster model, wherein the probability wind disaster model comprises a logarithmic mean value and a logarithmic standard deviation of a structural response peak value corresponding to the maximum wind speed; and calculating the failure probability of the power transmission tower under the maximum wind speed according to the probability wind disaster model. Through the method, the analysis process calculation is simple compared with the prior art, the analysis efficiency can be improved, and therefore the analysis of the wind damage vulnerability of the power transmission tower can be efficiently and conveniently obtained. In addition, the vulnerability curve obtained by the method is not jagged, so that additional smoothing treatment is not needed, and the steps are simpler.
Drawings
In order to more clearly illustrate the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart illustrating an embodiment of a method for analyzing vulnerability to wind damage of a power transmission tower according to the present application;
fig. 2 is a schematic structural diagram of an embodiment of the analysis device for wind damage vulnerability of the power transmission tower according to the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present application, the cloud game automatic acceleration method, apparatus and computer readable storage medium provided by the present application are further described in detail with reference to the accompanying drawings and the detailed description.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating an embodiment of an analysis method for wind damage vulnerability of a power transmission tower according to the present application. In this embodiment, the following steps may be included:
s110: and obtaining a wind speed time course sample and a power transmission tower model, wherein the power transmission tower model comprises the structural information and the material information of the power transmission tower.
A plurality of wind speed time-course records which accord with a preset wind speed range can be selected from the monitoring data of historical typhoon, wherein the maximum wind speed recorded by the wind speed time-course is in the preset wind speed range. Typically, the number of wind speed time interval records may be tens to hundreds.
Processing the wind speed time course record into a wind speed time course sample v with the same time step length and durationk(t) (k is 1,2, …, N), wherein N is the number of samples, and the peak value v of the wind speed time course of each wind speed time course sample is recordedp,k。
The power transmission tower model can be established based on the structural information and the material information of the power transmission tower. Alternatively, the transmission tower model may be built by large general finite element analysis software ANSYS.
S120: and processing the wind speed time course sample into a wind pressure time course, and carrying out nonlinear time course analysis on the power transmission tower model according to the wind pressure time course to obtain a probability wind disaster model, wherein the probability wind disaster model comprises a logarithmic mean value and a logarithmic standard deviation of a structural response peak value corresponding to the maximum wind speed.
And processing the wind speed time course sample into a wind pressure time course, and applying the wind pressure time course to the power transmission tower model. Optionally, a Newmark-beta method can be adopted to perform nonlinear time-course analysis on the power transmission tower model under each wind pressure time course to obtain a structural response sample r of the power transmission towerk(t) (k ═ 1,2, …, N) and the corresponding structural response peak r is recordedp,k。
The Newmark-beta method, also known as the Newmark-beta method, is a method that generalizes the linear acceleration method.
Peak value v of time course based on wind speedp,kIs the abscissa, with the peak value r of the structural responsep,kIs ordinate, and is obtained from discrete points (lnv) in the logarithmic planep,k，lnrp,k) (k is 1,2, …, N) and obtaining the log mean value of the structural response peak by adopting a linear regression method
Sum log standard deviation
Specifically, the structural response peak r can be obtained by employing a linear regression techniquep,kObtaining the logarithmic mean value of the structural response peak value through the relation between the logarithmic plane and the ground peak acceleration
The log mean of the structural response peaks is:
wherein a and b are linear regression coefficients, vpThe peak value of the wind speed time interval is obtained; the logarithmic standard deviation of the structural response peaks is:
in this embodiment, v isk(t) denotes the kth wind speed time course sample, vpRepresenting the peak value of the wind speed time course, vp,kRepresenting the peak of the kth wind speed time course sample.
S130: and calculating the failure probability of the power transmission tower under the maximum wind speed according to the probability wind disaster model.
The structural response peak value r of the power transmission tower model under the wind speed time range can be assumedp,kObeying the lognormal distribution, according to (1) and (2), the log mean and the log standard deviation of the structural response peak value corresponding to the maximum wind speed can be respectively calculated, and then the failure probability of the transmission tower under the maximum wind speed can be calculated as follows:
wherein C is the index of the extreme state of the power transmission tower, mulnCAnd σlnCThe logarithmic mean and logarithmic standard deviation, respectively, of the extreme state index can be obtained by statistics using measured data of structural materials and dimensions.
The embodiment provides an analysis method for wind damage vulnerability of a power transmission tower, a probability wind damage model of the power transmission tower can be obtained through analysis of a small number of nonlinear time-course samples, a relation between a structural response peak value and the maximum wind speed in a probability meaning is established, and the failure probability of the power transmission tower at any wind speed can be easily obtained by using a lognormal cumulative distribution function in combination with the assumption that the structural response peak value obeys lognormal distribution. Through the method, the analysis process calculation is simple compared with the prior art, the analysis efficiency can be improved, and therefore the analysis of the wind damage vulnerability of the power transmission tower can be efficiently and conveniently obtained. In addition, the vulnerability curve obtained by the method is not jagged, so that additional smoothing treatment is not needed, and the steps are simpler.
Based on the analysis method for the wind damage of the power transmission tower, the application also provides an analysis device for the wind damage of the power transmission tower. Referring to fig. 2, fig. 2 is a schematic structural diagram of an embodiment of an analysis apparatus for wind damage vulnerability of a power transmission tower according to the present application. In this embodiment, the apparatus 200 for analyzing the vulnerability to wind damage of the power transmission tower may include: a preprocessing unit 210, a probabilistic wind damage model unit 220 and an analysis unit 230.
The preprocessing unit 210 may be used to obtain wind speed time course samples and a transmission tower model, wherein the transmission tower model may include structural information and material information of the transmission tower.
Optionally, the preprocessing unit 210 may select a plurality of wind speed time-course records meeting a preset wind speed range from the monitoring data of the historical typhoon, wherein the maximum wind speed of the wind speed time-course records is within the preset wind speed range; processing the wind speed time course record into a wind speed time course sample v with the same time step length and durationk(t) (k is 1,2, …, N), N is the number of samples, and the peak value v of the wind speed time course of each wind speed time course sample is recordedp,k。
The probability wind damage model unit 220 may be configured to process the wind speed time interval sample into a wind pressure time interval, and perform nonlinear time interval analysis on the power transmission tower model according to the wind pressure time interval to obtain a probability wind damage model. The probability wind damage model may include a logarithmic mean and a logarithmic standard deviation of a structural response peak corresponding to the maximum wind speed.
Optionally, the probability wind damage model unit 220 may perform nonlinear time-course analysis on the power transmission tower model under each wind pressure time-course by using a Newmark- β method to obtain a structural response sample r of the power transmission towerk(t) (k ═ 1,2, …, N) and the corresponding structural response peak r is recordedp,k。
Further, the logarithm of the peak value of the wind speed time course is taken as an abscissa, the logarithm of the peak value of the structural response is taken as an ordinate, and the sum of the wind speed time course and the logarithm of the peak value of the structural response is obtained in a logarithmic planeDiscrete points (lnv)p,k，lnrp,k) (k-1, 2, …, N) and obtaining the log mean and log standard deviation of the structural response peak by using a linear regression method.
Wherein the log mean of the structural response peak is:
in the formula (1), a and b are linear regression coefficients, vpThe peak value of the wind speed time course.
The logarithmic standard deviation of the structural response peaks is:
the analysis unit 230 may be configured to calculate a failure probability of the transmission tower at the maximum wind speed according to a probabilistic wind damage model.
Specifically, assuming that the structural response peak of the power transmission tower model in the wind speed time range obeys log-normal distribution, the log mean and the standard deviation of the structural response peak corresponding to the maximum wind speed can be respectively calculated according to the equation (1) and the equation (2), and then the failure probability of the power transmission tower at the maximum wind speed can be calculated as follows:
wherein C is the index of the extreme state of the power transmission tower, mulnCAnd σlnCThe logarithmic mean and logarithmic standard deviation, respectively, of the extreme state index can be obtained by statistics using measured data of structural materials and dimensions.
It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. In addition, for convenience of description, only a part of structures related to the present application, not all of the structures, are shown in the drawings. The step numbers used herein are also for convenience of description only and are not intended as limitations on the order in which the steps are performed. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first", "second", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (10)
1. A method for analyzing wind damage vulnerability of a power transmission tower is characterized by comprising the following steps:
obtaining a wind speed time-course sample and a power transmission tower model, wherein the power transmission tower model comprises structural information and material information of the power transmission tower;
processing the wind speed time course sample into a wind pressure time course, and carrying out nonlinear time course analysis on the power transmission tower model according to the wind pressure time course to obtain a probability wind disaster model, wherein the probability wind disaster model comprises a logarithmic mean value and a logarithmic standard deviation of a structural response peak value corresponding to the maximum wind speed;
and calculating the failure probability of the power transmission tower under the maximum wind speed according to the probability wind disaster model.
2. The analysis method of claim 1, wherein the obtaining wind speed time course samples comprises:
selecting a plurality of wind speed time-course records which accord with a preset wind speed range from historical typhoon monitoring data, wherein the maximum wind speed recorded by the wind speed time-course is within the preset wind speed range;
processing the wind speed time course record into a wind speed time course sample v with the same time step length and durationk(t) (k is 1,2, …, N), N is the number of samples, and the peak value v of the wind speed time course of each wind speed time course sample is recordedp，k。
3. The analysis method according to claim 1, wherein the performing a nonlinear time interval analysis on the power transmission tower model according to the wind pressure time interval to obtain the probabilistic wind damage model comprises:
carrying out nonlinear time-course analysis on the power transmission tower model under each wind pressure time course by adopting a Newmark-beta method to obtain a structural response sample r of the power transmission towerk(t) (k ═ 1,2, …, N) and the corresponding structural response peak r is recordedp，k；
Taking the logarithm of the peak value of the wind speed time course as an abscissa and the logarithm of the structural response peak value as an ordinate, obtaining discrete points (ln v) in a logarithmic planep，k，ln rp，k) (k-1, 2, …, N) and obtaining the log mean and log standard deviation of the structural response peak by using a linear regression method.
4. The analytical method according to claim 3,
the log mean of the structural response peaks is:
wherein a and b are linear regression coefficients, vpThe peak value of the wind speed time interval is obtained;
the logarithmic standard deviation of the structural response peak is:
5. the analysis method according to claim 4, wherein the failure probability of the transmission tower at the maximum wind speed is:
wherein C is the index of the extreme state of the power transmission tower, mulnCAnd σlnCRespectively, the logarithmic mean and the logarithmic standard deviation of the extreme state index.
6. An analytical equipment of transmission tower wind disaster vulnerability, its characterized in that includes:
the preprocessing unit is used for obtaining a wind speed time-course sample and a power transmission tower model, wherein the power transmission tower model comprises structural information and material information of the power transmission tower;
the probability wind disaster model unit is used for processing the wind speed time interval sample into a wind pressure time interval and carrying out nonlinear time interval analysis on the power transmission tower model according to the wind pressure time interval to obtain a probability wind disaster model, wherein the probability wind disaster model comprises a logarithmic mean value and a logarithmic standard deviation of a structural response peak value corresponding to the maximum wind speed;
and the analysis unit is used for calculating the failure probability of the power transmission tower under the maximum wind speed according to the probability wind disaster model.
7. The analysis device according to claim 6,
the preprocessing unit is also used for selecting a plurality of historical typhoon monitoring dataRecording a wind speed time course according with a preset wind speed range, wherein the maximum wind speed recorded by the wind speed time course is within the preset wind speed range; processing the wind speed time course record into a wind speed time course sample v with the same time step length and durationk(t) (k is 1,2, …, N), N is the number of samples, and the peak value v of the wind speed time course of each wind speed time course sample is recordedp，k。
8. The analysis device according to claim 7,
the probability wind disaster model unit is also used for carrying out nonlinear time-course analysis on the power transmission tower model under each wind pressure time course by adopting a Newmark-beta method to obtain a structural response sample r of the power transmission towerk(t) (k ═ 1,2, …, N) and the corresponding structural response peak r is recordedp，k(ii) a Taking the logarithm of the peak value of the wind speed time course as an abscissa and the logarithm of the structural response peak value as an ordinate, obtaining discrete points (ln v) in a logarithmic planep，k，ln rp，k) (k-1, 2, …, N) and obtaining the log mean and log standard deviation of the structural response peak by using a linear regression method.
9. The analysis device according to claim 8,
the log mean of the structural response peaks is:
wherein a and b are linear regression coefficients, vpThe peak value of the wind speed time interval is obtained;
the logarithmic standard deviation of the structural response peak is:
10. the analysis device according to claim 9, wherein the failure probability of the transmission tower at the maximum wind speed is:
wherein C is the index of the extreme state of the power transmission tower, mulnCAnd σlnCRespectively, the logarithmic mean and the logarithmic standard deviation of the extreme state index.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105740549A (en) * | 2016-02-01 | 2016-07-06 | 西安交通大学 | Wind vibration simulation method of 220kV power transmission line tower line coupled system |
| CN107330233A (en) * | 2017-08-31 | 2017-11-07 | 广东电网有限责任公司电力科学研究院 | A kind of power transmission tower design wind speed analysis method and device |
| US20190303783A1 (en) * | 2016-06-09 | 2019-10-03 | Hitachi, Ltd. | Data prediction system and data prediction method |
| CN111783326A (en) * | 2020-05-28 | 2020-10-16 | 东南大学 | Wind-induced safety evaluation method for power transmission tower structure based on wind speed and dynamic response test |
| CN112380748A (en) * | 2020-11-13 | 2021-02-19 | 广东电网有限责任公司电力科学研究院 | Wind-induced random vibration analysis method and related device for power transmission tower |
-
2021
- 2021-03-31 CN CN202110353006.7A patent/CN113011770B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105740549A (en) * | 2016-02-01 | 2016-07-06 | 西安交通大学 | Wind vibration simulation method of 220kV power transmission line tower line coupled system |
| US20190303783A1 (en) * | 2016-06-09 | 2019-10-03 | Hitachi, Ltd. | Data prediction system and data prediction method |
| CN107330233A (en) * | 2017-08-31 | 2017-11-07 | 广东电网有限责任公司电力科学研究院 | A kind of power transmission tower design wind speed analysis method and device |
| CN111783326A (en) * | 2020-05-28 | 2020-10-16 | 东南大学 | Wind-induced safety evaluation method for power transmission tower structure based on wind speed and dynamic response test |
| CN112380748A (en) * | 2020-11-13 | 2021-02-19 | 广东电网有限责任公司电力科学研究院 | Wind-induced random vibration analysis method and related device for power transmission tower |
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